Attempts have been made to develop image compression techniques by identifying and utilizing visual features within images to achieve higher coding efficiency. Moreover, an awareness of how the human visual system (HVS) perceives various image features has been incorporated into coding methods for removing some of the visual redundancy inherent in images and for improving the visual quality of resulting images. Nonetheless, the development of such coding schemes is greatly influenced by the effectiveness of edge detection and segmentation tools.
Meanwhile, great improvements have also been made in conventional signal-processing-based compression methods. Such mainstream coding schemes manage to make use of the statistical redundancy among pixels in the pursuit of high coding efficiency. State-of-the-art JPEG2000 and MPEG-4 AVC/H.264 are two such examples that greatly outperform previous generations in coding efficiency. However, perceptual quality is almost completely neglected during algorithm design. Additionally, in current development of such schemes, even small improvements come at a high cost of multiplying encoding complexity.
Image inpainting (also known as image completion) is a process of restoring missing data in a designated region of an image in a visually plausible manner. Current results show that inpainting can recover homogenous regions in a natural-looking manner, even when certain kinds of limited structure are present. However, conventional image inpainting is not effective at regenerating significant visual structure (e.g., structural edges), especially if they are unique or have special, exact placement in the image. These structural edges are conventionally relegated to conventional compression—so that they will reliably reappear in the regenerated image.
Nonetheless, structural data, especially edges, have a perceptual significance that is greater than the numerical value of their energy contribution to the entire image. Thus, the coding efficiency and the video quality of image coding techniques could be improved if the structural information might be properly exploited. What is needed is way to efficiently capture and organize structural edge information extracted from a source image so that an inpainter in a decoder can restore relatively large structural regions of the image with guidance that occupies very little data space/minimal bitstream bandwidth to transmit from encoder to decoder.